Abstract: A set of input images are acquired sequentially as image tensors. A low-tubal rank tensor and a sparse tensor are initialized using the image tensor, wherein the low-tubal rank tensor is a tensor product of a low-rank spanning tensor basis and corresponding tensor coefficients, and for each image, updating iteratively the image tensor, the tensor coefficients, and the sparse tensor using the image tensor and the low-rank spanning basis from a previous iteration. The spanning tensor basis is updated using the tensor coefficients, the sparse tensor, and the low rank tubal tensor, wherein the low rank tubal tensor represents a set of output images and the sparse tensor representing a set of sparse images.

Abstract: Systems and methods for image processing including a memory having stored multi-angled view images of a scene. Each multi-angled view image includes pixels, and at least one multi-angled view image includes a clouded area in the scene, resulting in missing pixels. A processor configured to align three multi-angled view images in the multi-angled view images to a target view angle of the scene, to form a set of aligned multi-angled view images representing a target point of view of the scene, at least one aligned multi-angled view image of the at least three multi-angled view images, has missing pixels due to the clouded area. Form a matrix using vectorized aligned multi-angled view images, wherein the matrix is incomplete due to the missing pixels. Complete the matrix using a matrix completion to combine the aligned multi-angled view images to produce a fused image of the scene without the clouded area.

Abstract: An image of a region of interest (ROI) is generated by a radar system including a set of one or more antennas. The radar system has unknown position perturbations. Pulses are transmitted, as a source signal, to the ROI using the set of antennas at different positions and echoes are received, as a reflected signal, by the set of antennas at the different positions. The reflected signal is deconvolved with the source signal to produce deconvolved data. The deconvolved data are compensated according a coherence between the reflected signal to produce compensated data. Then, a procedure is applied to the compensated data to produce reconstructed data, which are used to reconstruct auto focused images.

Abstract: Systems and methods for an imaging system including a first sensor to acquire a sequence of images of a first modality. A memory to store a first convolutional memory matrix. Wherein each element of the first convolutional memory matrix is a convolutional function of correspondingly located elements of coefficient matrices of convolutional representation of the images of the first modality, and to store a first dictionary matrix including atoms of the images of the first modality. A processor to transform a first image of a scene acquired by the first sensor as a convolution of the first dictionary matrix and a first coefficient matrix, to update the elements of the first convolutional memory matrix with the convolutional function of correspondingly located non-zero elements of the first coefficient matrix, and to update the dictionary matrix using the updated first convolutional memory matrix.

Abstract: Systems and methods for a computer implemented image reconstruction system that includes an input interface to receive measurements of a scene. A memory to store a sparsity enforcing neural network (SENN) formed by layers of nodes propagating messages through the layers. Wherein at least one node of the SENN modifies an incoming message with a non-linear function to produce an outgoing message and propagates the outgoing message to another node of the SENN. Wherein the non-linear function is a dual-projection function that limits the incoming message if the incoming message exceeds a threshold. Such that, the SENN is trained to reconstruct an image of the scene from the measurements of the scene. A processor to process the measurements with the SENN to reconstruct the image of the scene. Finally, an output interface to render the reconstructed image of the scene.

Abstract: A method and system for generating a high resolution two-dimensional (2D) radar image, by first transmitting a radar pulse by a transmit antenna at an area of interest and receiving echoes, corresponding to reflection the radar pulse in the area of interest, at a set of receive arrays, wherein each array includes and a set of receive antennas that are static and randomly distributed at different locations at a same side of the area of interest with a random orientation within a predetermined angular range. The the echoes are sampled for each receive array to produce distributed data for each array. Then, a compressive sensing (CS) procedure is applied to the distributed data to generate the high resolution 2D radar image.

Abstract: Systems and methods for image processing including a memory having stored multi-angled view images of a scene. Each multi-angled view image includes pixels, and at least one multi-angled view image includes a clouded area in the scene, resulting in missing pixels. A processor configured to align three multi-angled view images in the multi-angled view images to a target view angle of the scene, to form a set of aligned multi-angled view images representing a target point of view of the scene, at least one aligned multi-angled view image of the at least three multi-angled view images, has missing pixels due to the clouded area. Form a matrix using vectorized aligned multi-angled view images, wherein the matrix is incomplete due to the missing pixels. Complete the matrix using a matrix completion to combine the aligned multi-angled view images to produce a fused image of the scene without the clouded area.

Abstract: A system and method determines a noise free image of a scene located behind a wall. A transmit antenna emits a radar pulse from different locations in front of the wall, wherein the radar pulses propagate through the wall and are reflected by the scene as echoes. A set of stationary receive antennas acquire the echoes corresponding to each pulse transmitted from each different location. A radar imaging subsystem connected to the transmit antenna and the set of receive antennas determines a noisy image of the scene for each location of the transmit antenna. A total variation denoiser denoises each noisy image to produce a corresponding denoised image. A combiner combines incoherently the denoised images to produce the noise free image.

Abstract: A method and system generates a three-dimensional (3D) image by first acquiring data from a scene using multiple parallel baselines and multiple different pulse repetition frequencies (PRF), wherein the multiple baselines are arranged in a hyperplane. Then, a 3D compressive sensing reconstruction procedure is applied to the data to generate the 3D image corresponding to the scene.

Abstract: A method generates a three-dimensional (3D) scene image of a scene using a MIMO array including a set of antenna by first selecting a subsets of the antennas as transmit antennas and receive antennas. Radio frequency (RF) signal are transmitted into the scene using the subset of transmit antennas while the MIMO array is moving at a varying velocity. The RF signal are received at the subset of receive antennas as MIMO data, which is aligned and regularized. Then, a compressive sensing (CS)-based reconstruction procedure is applied to the aligned MIMO data to generate the 3D image of the scene.

Abstract: Systems and methods for fusing a radar image in response to radar pulses transmitted to a region of interest (ROI). The method including receiving a set of reflections from a target located in the ROI. Each reflection is recorded by a receiver at a corresponding time and at a corresponding coarse location. Aligning the set of reflections on a time scale using the corresponding coarse locations of the set of distributed receivers to produce a time projection of the set of reflections for the target. Fitting a line into data points formed from radar pulses in the set of reflections. Determining a distance between the fitted line and each data point. Adjusting the coarse position of the set of distributed receivers using the corresponding distance between the fitted line and each data point. Fusing the radar image using the set of reflections received at the adjusted coarse position.

Abstract: A method generates a 3D synthetic aperture radar (SAR) image of an area by first acquiring multiple data sets from the area using one or more SAR systems, wherein each SAR system has one or more parallel baselines and multiple pulse repetition frequency (PRF), wherein the PRF for each baseline is different. The data sets are registered and aligned to produce aligned data sets. Then, a 3D compressive sensing reconstruction procedure is applied to the aligned data sets to generate the 3D image corresponding to the area.

Abstract: A method propagates a pulse of wave through the material to receive a set of echoes resulted from scattering the pulse by different portions of the material and simulates a propagation of the pulse in the material using a neural network to determine a simulated set of echoes. Each node in a layer of the neural network corresponds to a portion of the material and assigned a value the permittivity of the portion of the material, such that the values of the nodes at locations of the portions form the image of the distribution of the permittivity of the material. The connection between two layers in the neural network models a scattering event. The method updates the values of the nodes by reducing an error between the received set of echoes and the simulated set of echoes to produce an image of the distribution of the permittivity of the material.

Abstract: An image of a region of interest (ROI) is generated by a radar system including a set of one or more antennas. The radar system has unknown position perturbations. Pulses are transmitted, as a source signal, to the ROI using the set of antennas at different positions and echoes are received, as a reflected signal, by the set of antennas at the different positions. The reflected signal is deconvolved with the source signal to produce deconvolved data. The deconvolved data are compensated according a coherence between the reflected signal to produce compensated data. Then, a procedure is applied to the compensated data to produce reconstructed data, which are used to reconstruct auto focused images.

Abstract: A method detects faults during a steady state of an operation of an induction motor. The method measures, in a time domain, a signal of a current powering the induction motor with a fundamental frequency and determines, in a frequency domain, a set of frequencies with non-zero amplitudes, such that a reconstructed signal formed by the set of frequencies with non-zero amplitudes approximates the signal measured in the time domain. The determining includes a compressive sensing via searching within a subband including the fundamental frequency of the signal subject to condition of a sparsity of the signal in the frequency domain. The method detects a fault in the induction motor if the set of frequencies includes a fault frequency different from the fundamental frequency.

Abstract: A set of input images are acquired sequentially as image tensors. A low-tubal rank tensor and a sparse tensor are initialized using the image tensor, wherein the low-tubal rank tensor is a tensor product of a low-rank spanning tensor basis and corresponding tensor coefficients, and for each image, updating iteratively the image tensor, the tensor coefficients, and the sparse tensor using the image tensor and the low-rank spanning basis from a previous iteration. The spanning tensor basis is updated using the tensor coefficients, the sparse tensor, and the low rank tubal tensor, wherein the low rank tubal tensor represents a set of output images and the sparse tensor representing a set of sparse images.

Abstract: A method and system for generating a high resolution two-dimensional (2D) radar image, by first transmitting a radar pulse by a transmit antenna at an area of interest and receiving echoes, corresponding to reflection the radar pulse in the area of interest, at a set of receive arrays, wherein each array includes and a set of receive antennas that are static and randomly distributed at different locations at a same side of the area of interest with a random orientation within a predetermined angular range. The the echoes are sampled for each receive array to produce distributed data for each array. Then, a compressive sensing (CS) procedure is applied to the distributed data to generate the high resolution 2D radar image.

Abstract: A method detects faults during a steady state of an operation of an induction motor. The method measures, in a time domain, a signal of a current powering the induction motor with a fundamental frequency and determines, in a frequency domain, a set of frequencies with non-zero amplitudes, such that a reconstructed signal formed by the set of frequencies with non-zero amplitudes approximates the signal measured in the time domain. The determining includes a compressive sensing via searching within a subband including the fundamental frequency of the signal subject to condition of a sparsity of the signal in the frequency domain. The method detects a fault in the induction motor if the set of frequencies includes a fault frequency different from the fundamental frequency.

Abstract: A method and system generates a three-dimensional (3D) image by first acquiring data from a scene using multiple parallel baselines and multiple different pulse repetition frequencies (PRF), wherein the multiple baselines are arranged in a hyperplane. Then, a 3D compressive sensing reconstruction procedure is applied to the data to generate the 3D image corresponding to the scene.

Abstract: A method generates a three-dimensional (3D) scene image of a scene using a MIMO array including a set of antenna by first selecting a subsets of the antennas as transmit antennas and receive antennas. Radio frequency (RF) signal are transmitted into the scene using the subset of transmit antennas while the MIMO array is moving at a varying velocity. The RF signal are received at the subset of receive antennas as MIMO data, which is aligned and regularized. Then, a compressive sensing (CS)-based reconstruction procedure is applied to the aligned MIMO data to generate the 3D image of the scene.